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• Explainability of Agent-based Models as a Tool for Validation and Exploration

  Project ID: STAI-CDT-2023-KCL-11
  Themes: Argumentation, Verification
  Supervisor: Dr Steffen Zschaler, Dr Katie Bentley

  Agent-based models (ABMs) are an AI technique to help improve our understanding of complex real-world interactions and their “emergent behaviours”. ABMs are used to develop and test theories or to explore how interventions...

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• Computational Social Choice and Machine Learning for Ethical Decision Making

  Project ID: STAI-CDT-2023-KCL-5
  Themes: AI Planning, Argumentation, Norms, Reasoning
  Supervisor: Maria Polukarov

  The problem of ethical decision making presents a grand challenge for modern AI research. Arguably, the main obstacle to automating ethical decisions is the lack of a formal specification of ground-truth ethical principles,...

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• Creating and evolving knowledge graphs at scale for explainable AI

  Project ID: STAI-CDT-2022-KCL-1
  Themes: AI Provenance, Argumentation, Verification
  Supervisor: Prof Elena Simperl

  Knowledge graphs and knowledge bases are forms of symbolic knowledge representations used across AI applications. Both refer to a set of technologies that organise data for easier access, capture information about people,...

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• Argumentation-based Interactive Explainable Scheduling

  Project ID: STAI-CDT-2021-IC-11
  Themes: Argumentation
  Supervisor: Ruth Misener

  AI is continuing to make progress in many settings, fuelled by data availability and computational power, but it is widely acknowledged that it cannot fully benefit society without addressing its widespread inability to...

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